New Allometric Equations for Arctic Shrubs and Their Application for Calculating the Albedo of Surfaces with Snow and Protruding Branches
Bibliographic record
Abstract
Abstract Arctic shrubs reduce surface albedo in winter when branches protrude above the snow. To calculate the albedo of those mixed surfaces, the branch area index (BAI) of Arctic shrubs needs to be known. Moreover, an exposed-vegetation function is required to determine the BAI for protruding branches only. This study used a structural analysis of 30Betula glandulosashrubs, sampled near Umiujaq, northern Quebec, to (i) establish an allometric relationship between shrub height and BAI and (ii) determine a specific exposed-vegetation function for Arctic shrubs. The spectral albedo (400–1080 nm) of mixed surfaces was then simulated with the equations derived from this study and validated with in situ measured spectra. Shrubs were sampled from two sites, one along the coast and the other in a nearby valley. The shrub height–BAI relationship varied between both sites. BAI values of shrubs growing in the wind-sheltered valley were 30%–50% lower. The exposed-vegetation function obtained here differed from the linear functions found in the literature. The linear functions strongly overestimated the BAI of exposed branches. Albedo was well simulated with an accuracy of 3% when using an allometric relationship adapted to the environmental conditions of our study site. However, simulated albedo values were consistently higher than field measurements, probably because radiation absorbed by impurities and buried branches was neglected in the model. We conclude that specific exposed-vegetation and allometric equations need to be implemented in models to accurately simulate the albedo of mixed snow–shrub surfaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".